---
title: Netmind
---

This will help you get started with Netmind embedding models using LangChain. For detailed documentation on `NetmindEmbeddings` features and configuration options, please refer to the [API reference](https://python.langchain.com/api_reference/).

## Overview

### Integration details

| Provider | Package |
|:--------:|:-------:|
| [Netmind](/oss/integrations/providers/netmind/) | [langchain-netmind](https://python.langchain.com/api_reference/) |

## Setup

To access Netmind embedding models you'll need to create a/an Netmind account, get an API key, and install the `langchain-netmind` integration package.

### Credentials

Head to [www.netmind.ai/](https://www.netmind.ai/) to sign up to Netmind and generate an API key. Once you've done this set the NETMIND_API_KEY environment variable:

```python
import getpass
import os

if not os.getenv("NETMIND_API_KEY"):
    os.environ["NETMIND_API_KEY"] = getpass.getpass("Enter your Netmind API key: ")
```

If you want to get automated tracing of your model calls you can also set your [LangSmith](https://docs.smith.langchain.com/) API key by uncommenting below:

```python
# os.environ["LANGCHAIN_TRACING_V2"] = "true"
# os.environ["LANGCHAIN_API_KEY"] = getpass.getpass("Enter your LangSmith API key: ")
```

### Installation

The LangChain Netmind integration lives in the `langchain-netmind` package:

```python
%pip install -qU langchain-netmind
```

```output
[notice] A new release of pip is available: 24.0 -> 25.0.1
[notice] To update, run: pip install -U pip
Note: you may need to restart the kernel to use updated packages.
```

## Instantiation

Now we can instantiate our model object:

```python
from langchain_netmind import NetmindEmbeddings

embeddings = NetmindEmbeddings(
    model="nvidia/NV-Embed-v2",
)
```

## Indexing and Retrieval

Embedding models are often used in retrieval-augmented generation (RAG) flows, both as part of indexing data as well as later retrieving it. For more detailed instructions, please see our [RAG tutorials](/oss/langchain/rag).

Below, see how to index and retrieve data using the `embeddings` object we initialized above. In this example, we will index and retrieve a sample document in the `InMemoryVectorStore`.

```python
# Create a vector store with a sample text
from langchain_core.vectorstores import InMemoryVectorStore

text = "LangChain is the framework for building context-aware reasoning applications"

vectorstore = InMemoryVectorStore.from_texts(
    [text],
    embedding=embeddings,
)

# Use the vectorstore as a retriever
retriever = vectorstore.as_retriever()

# Retrieve the most similar text
retrieved_documents = retriever.invoke("What is LangChain?")

# show the retrieved document's content
retrieved_documents[0].page_content
```

```output
'LangChain is the framework for building context-aware reasoning applications'
```

## Direct Usage

Under the hood, the vectorstore and retriever implementations are calling `embeddings.embed_documents(...)` and `embeddings.embed_query(...)` to create embeddings for the text(s) used in `from_texts` and retrieval `invoke` operations, respectively.

You can directly call these methods to get embeddings for your own use cases.

### Embed single texts

You can embed single texts or documents with `embed_query`:

```python
single_vector = embeddings.embed_query(text)
print(str(single_vector)[:100])  # Show the first 100 characters of the vector
```

```output
[-0.0051240199245512486, -0.01726294495165348, 0.011966848745942116, -0.0018107350915670395, 0.01146
```

### Embed multiple texts

You can embed multiple texts with `embed_documents`:

```python
text2 = (
    "LangGraph is a library for building stateful, multi-actor applications with LLMs"
)
two_vectors = embeddings.embed_documents([text, text2])
for vector in two_vectors:
    print(str(vector)[:100])  # Show the first 100 characters of the vector
```

```output
[-0.0051240199245512486, -0.01726294495165348, 0.011966848745942116, -0.0018107350915670395, 0.01146
[0.022523142397403717, -0.002223758026957512, -0.008578270673751831, -0.006029821466654539, 0.008752
```

## API Reference

For detailed documentation on `NetmindEmbeddings` features and configuration options, please refer to the:

* [API reference](https://python.langchain.com/api_reference/)
* [langchain-netmind](https://github.com/protagolabs/langchain-netmind)
* [pypi](https://pypi.org/project/langchain-netmind/)

```python

```
